湖北民族大学学报（自然科学版）2025，Vol.43Issue(2)：196-201,6.DOI:10.13501/j.cnki.42-1908/n.2025.06.025
基于双路径多层混合网络的肺纤维化病灶区域分类方法
Regional Classification Method of Pulmonary Fibrosis Lesions Based on Dual Pathway Multi-level Hybrid Network
摘要
Abstract
To address the diagnostic challenges posed by the structural complexity and morphological heterogeneity of pulmonary fibrosis lesions in computed tomography(CT)images,a region classification method of pulmonary fibrosis lesions based on dual pathway multi-layer hybrid network(DMH-Net)was proposed.Firstly,a texture feature enhancement module was designed to quantify the density gradient distribution of lesion micro-textures,and a calibration mechanism was used to optimize the modeling efficiency of the vision transformer(ViT)for morphological features.Secondly,a dynamic mamba transformer(DMTransformer)encoder based on mamba-like linear attention(MLLA)and dynamic hyperbolic tangent activation was constructed to achieve pixel-level lesion boundary localization.Finally,a method of global-local tandem dual-path information interaction was built to realize the coupled expression of macroscopic morphological features and microscopic texture features.The results showed that the accuracy of the DMH-Net model was improved by 16.87%compared with the ViT model.This study provided a new technical paradigm for the intelligent diagnosis of pulmonary fibrosis.关键词
肺纤维化/卷积神经网络/Transformer/CT图像/纹理特征/多尺度/间质Key words
pulmonary fibrosis/convolutional neural network/Transformer/CT image/texture feature/multiscale/interstitial分类
计算机与自动化引用本文复制引用
苏树智,尹欣乐,郑雪佳,戴勇..基于双路径多层混合网络的肺纤维化病灶区域分类方法[J].湖北民族大学学报（自然科学版）,2025,43(2):196-201,6.基金项目
国家自然科学基金项目(52374155) （52374155）
安徽理工大学医学专项培育项目(YZ2023H2A007) （YZ2023H2A007）
合肥综合性国家科学中心大健康研究院职业医学与健康联合研究中心项目(OMH-2023-05,OMH-2023-24). （OMH-2023-05,OMH-2023-24）
